code: def simple_tmle(y, w, q0w, q1w, p, alpha=0.0001):
      """Calculate the ATE and variances with the simplified TMLE method.
  
      Args:
          y (numpy.array): an outcome vector
          w (numpy.array): a treatment vector
          q0w (numpy.array): an outcome prediction vector given no treatment
          q1w (numpy.array): an outcome prediction vector given treatment
          p (numpy.array): a propensity score vector
          alpha (float, optional): a clipping threshold for predictions
  
      Returns:
          (tuple)
  
              - ate (float): ATE
              - se (float): The standard error of ATE
      """
      scaler = MinMaxScaler()
      ystar = scaler.fit_transform(y.reshape(-1, 1)).flatten()
  
      q0 = np.clip(
          scaler.transform(q0w.reshape(-1, 1)).flatten(),
          alpha,
          1 - alpha,
      )
      q1 = np.clip(
          scaler.transform(q1w.reshape(-1, 1)).flatten(),
          alpha,
          1 - alpha,
      )
      qaw = q0 * (1 - w) + q1 * w
      intercept = logit(qaw)
  
      h1 = w / p
      h0 = (1 - w) / (1 - p)
      sol = minimize(
          logit_tmle,
          np.zeros(2),
          args=(ystar, intercept, h0, h1),
          method="Newton-CG",
          jac=logit_tmle_grad,
          hess=logit_tmle_hess,
      )
  
      qawstar = scaler.inverse_transform(
          expit(intercept + sol.x[0] * h0 + sol.x[1] * h1).reshape(
              -1, 1
          )
      ).flatten()
      q0star = scaler.inverse_transform(
          expit(logit(q0) + sol.x[0] / (1 - p)).reshape(-1, 1)
      ).flatten()
      q1star = scaler.inverse_transform(
          expit(logit(q1) + sol.x[1] / p).reshape(-1, 1)
      ).flatten()
  
      ic = (
          (w / p - (1 - w) / (1 - p)) * (y - qawstar)
          + q1star
          - q0star
          - np.mean(q1star - q0star)
      )
  
      return np.mean(q1star - q0star), np.sqrt(np.var(ic) / np.size(y))
  
file_path: causalml/causalml/inference/meta/tmle.py
imports: import logging
  import numpy
  import pandas
  from scipy.optimize import minimize
  from scipy.special import expit, logit
  from scipy.stats import norm
  from sklearn.preprocessing import MinMaxScaler
  from causalml.inference.meta.utils import check_treatment_vector, check_p_conditions, convert_pd_to_np
  from causalml.propensity import calibrate
label_desc: Use image node_id 4 for calling a global function with example usage: simple_tmle(y, w, q0w, q1w, p, alpha) and returns: np,  np